Classification and Categorization of COVID-19 Outbreak in Pakistan

Abstract

Coronavirus is a potentially fatal disease that normally occurs in mammals and birds. Generally, in humans, the virus spreads through aerial droplets of any type of fluid secreted from the body of an infected person. Coronavirus is a family of viruses that is more lethal than other unpremed-itated viruses. In December 2019, a new variant, i.e., a novel coronavirus (COVID-19) developed in Wuhan province, China. Since January 23, 2020, the number of infected individuals has increased rapidly, affecting the health and economies of many countries, including Pakistan. The objective of this research is to provide a system to classify and categorize the COVID-19 outbreak in Pakistan based on the data collected every day from different regions of Pakistan. This research also compares the performance of machine learning classifiers (i.e., Decision Tree (DT), Naive Bayes (NB), Support Vector Machine, and Logistic Regression) on the COVID-19 dataset collected in Pakistan. According to the experimental results, DT and NB classifiers out-performed the other classifiers. In addition, the classified data is categorized by implementing a Bayesian Regularization Artificial Neural Network (BRANN) classifier. The results demonstrate that the BRANN classifier outperforms state-of-the-art classifiers.

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Computers, Materials & Continua
DOI:10.32604/cmc.2021.015655
Article
Classication and Categorization of COVID-19 Outbreak in Pakistan
Amber Ayoub1, Kainaat Mahboob1, Abdul Rehman Javed2, Muhammad Rizwan1,
Thippa Reddy Gadekallu2, Mustufa Haider Abidi3,*and Mohammed Alkahtani4,5
1Department of Computer Science, Kinnaird College for Women, Lahore, 54000, Pakistan
2Department of Cyber Security, Air University, Islamabad, Pakistan
3School of Information Technology and Engineering, Vellore Institute of Technology, Tamil Nadu, India
4Raytheon Chair for Systems Engineering, Advanced Manufacturing Institute, King Saud University,
Riyadh, 11421, Saudi Arabia
5Industrial Engineering Department, College of Engineering, King Saud University, Riyadh, 11421, Saudi Arabia
*Corresponding Author: Mustufa Haider Abidi. Email: mabidi@ksu.edu.sa
Received: 01 December 2020; Accepted: 05 February 2021
Abstract: Coronavirus is a potentially fatal disease that normally occurs in
mammals and birds. Generally, in humans, the virus spreads through aerial
droplets of any type of uid secreted from the body of an infected person.
Coronavirus is a family of viruses that is more lethal than other unpremed-
itated viruses. In December 2019, a new variant, i.e., a novel coronavirus
(COVID-19) developed in Wuhan province, China. Since January 23, 2020,
the number of infected individuals has increased rapidly, affecting the health
and economies of many countries, including Pakistan. The objective of this
research is to provide a system to classify and categorize the COVID-19
outbreak in Pakistan based on the data collected every day from different
regions of Pakistan. This research also compares the performance of machine
learning classiers (i.e., Decision Tree (DT), Naive Bayes (NB), Support Vec-
tor Machine, and Logistic Regression) on the COVID-19 dataset collected in
Pakistan. According to the experimental results, DT and NB classiers out-
performed the other classiers. In addition, theclassied data is categorized by
implementing a Bayesian Regularization Articial Neural Network (BRANN)
classier. The results demonstrate that the BRANN classier outperforms
state-of-the-art classiers.
Keywords: COVID-19; pandemic; neural network; BRANN; machine
learning
1 Introduction
The COVID-19 outbreak that appeared in Wuhan, China at the end of December 2019 was
initially considered a pneumonia based on etiology. The virus soon spread worldwide at a rapid
rate [1]. On January 30, 2020, the World Health Organization (WHO) declared the COVID-19
outbreak a Public Health Emergency of International Concern [2,3]. This virus has affected
people in more than 209 nations around the world. The overheads of the coronavirus outbreak
This work is licensed under a Creative Commons Attribution 4.0 International License,
which permits unrestricted use, distribution, and reproduction in any medium, provided
the original work is properly cited.

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are continually increasing. When this virus rst started to spread, there were approximately 600
conrmed cases in China. Globally, the number of people who have died because of this virus has
been increasing daily [4]. The WHO determined that the most common symptoms of this virus
are tiredness, fever, and dry cough [5]. Most people with these symptoms can recover without
extraordinary treatment or prescriptions. However, some patients have more severe symptoms,
such as a runny nose, sore throat, nasal congestion, and general or severe pain. Typically, 80%
of people who became infected have severe symptoms [6]. In the United Kingdom, the National
Health Service (NHS) has reported cases with more severe side effects, including high fever and
persistent cough. The NHS recommends that anybody with these sorts of symptoms should self-
quarantine for 7 to 14 days [7]. The infection spreads between individuals in close contact who
are exposed to respiratory aerosol droplets that are emitted, primarily when an infected person
coughs or sneezes, or shouts, sings, or talks.
For the most part, the droplets do not travel signicant distances. Typically, they fall to
the ground or onto immediate surfaces. Transmission may also occur through little droplets that
can remain suspended in the air for longer periods of time [8]. People may become infected by
touching a contaminated surface and then touching their face [9]. Outbreaks and rapid spread are
highly expected, even before symptoms are noticeable, and from individuals who do not possess
any symptoms of being infected by the virus, but they carry it [10]. Fig. 1 represents the worldwide
spread of this coronavirus. It is believed that the virus did not spread in Pakistan the way it
spread in other countries, like China, the USA, and Italy. Pakistan, with permeable borders, is
sandwiched between two focal points of this coronavirus (China and Iran).
Figure 1: Worldwide spread of COVID-19
Recently, Pakistan has reinforced their precautions against COVID-19 by various strategies,
such as detailing the use of national crisis readiness, compulsory thermal screenings at all entry
points, observation of regional spread, contact tracing, and information assortment through
various sources. Testing has been reinforced by bringing in Polymerase Chain Reaction units
for SARS-COV-2 diagnostics [11]. Assets have been deployed to setup quarantine centers in
preparation of expected cases. Locations for these stations include a few urban areas, emergency
clinics, and reconnaissance units that have been actuated to track the contacts of afrmed cases,
as suggested by the WHO [10]. The COVID-19 infection has spread to more than 213 nations,
and as of April 17, 2020, there were 1,995,983 conrmed cases and 131,037 deaths [12].
Pakistan revealed its initial two positive cases on February 26, 2020. These cases were con-
nected to travel to Iran [13]. The number of positive cases across the nation rose to 7,025 on
April 17th , 2020: 3,276 positive cases and 135 deaths in Punjab, 2,008 cases in Sindh, 993 cases
in Khyber Pakhtunkhwa, 303 cases in Balochistan, 237 cases in Gilgit Baltistan, 154 cases in
Islamabad Capital Territory (ICT), and 46 cases in Azad Jammu Kashmir [14].

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The number of positive cases is rising rapidly every day. In fact, in most countries, the number
of cases is probably much higher than recorded, due to limited testing [14,15]. Fig. 2 shows the
number of total coronavirus cases in Pakistan. The exponential increase in cases has driven the
Government to force total and severe lockdowns in numerous urban areas [16].
Figure 2: COVID-19 cases in Pakistan
Fig. 3 shows the total number of COVID-19 cases, the total number of deaths, and the total
number of recovered cases in different regions of Pakistan.
Figure 3: Total recovered cases, deaths, and conrmed cases in Pakistan
1.1 Problem Statement
Deaths due to COVID-19 are increasing day by day in Pakistan. The nature of the COVID-19
outbreak differs in various countries. For example, in China, Iran, and France, COVID-19 out-
break is characterized by extremely high numbers and severe cases. The outbreak severity can
be detected through an increase in the number of deaths. Thus, in this research, the nature of

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the outbreak is detected with the help of the COVID-19 dataset for the past few months in
Pakistan collected by the Government. If the nature of the COVID-19 outbreak can be detected
from the past months’ death rate, then with the help of standard operating procedure and
precautionary measures, the death rate can be reduced in the coming months in Pakistan. For
outbreak detection, the COVID-19 dataset is rst classied with machine learning (ML) classiers.
Then the classied dataset is categorized into severe and normal COVID-19 outbreaks, using the
Bayesian regularized articial neural network (BRANN) classier.
1.2 Motivation and Contribution
The COVID-19 death rate is high and is increasing day by day globally [17]. This research
is intended to classify and categorize the nature of the outbreak in Pakistan using machine
learning classiers. In this study, a dataset of COVID-19 patients from different regions (primarily
populated regions) of Pakistan is preprocessed and then classied to understand the nature of
the virus and its outbreak in Pakistan. Machine learning classiers: Decision Tree (DT), Naive
Bayes (NB), Support Vector Machine (SVM), and Logistic Regression (LR) are implemented,
and results are compared based on performance measures (i.e., accuracy, precision, and recall).
The comparison of machine learning classiers indicates that the DT and NB classiers return
100% accuracy. Classied data is input to the BRANN to categorize the COVID-19 outbreak in
Pakistan to determine if the nature of the outbreak will be normal or severe.
The remainder of this paper is organized as follows. Section 2 discusses the related work.
Section 3 provides the proposed methodology to classify and categorize COVID patients. Section 4
provides the experimental analysis and results. Conclusions and suggestions for future work are
presented in Section 5.
2 Literature Review
COVID-19 virus was initially discovered in December 2019 in the population of Wuhan,
China. Later, it spread to other regions of China and other parts of the world [18]. Various
papers and studies have applied different techniques on COVID-19 datasets. In this section, several
studies that investigate the application of machine learning algorithms on different diseases are
discussed.
SVM and Mutual Information techniques have been applied to classify genes [19]. In that
study, the authors claimed that the SVM classier achieved the best mean accuracy rate. In
addition, the fuzzy KNN approach has been used on a Parkinson’s dataset to help generate a
diagnostic system that will make better clinical diagnostic decisions [20]. Here, researchers utilized
different machine learning techniques to propose a novel method. They computed signicant
features by implementing machine learning techniques to improve the accuracy rate of predicting
cardiovascular disease. Their prediction model gives 88.7% accuracy [21]. In 2015, a combination
of SVM and fuzzy logic was applied for the risk classication of diabetes. Fuzzy reasoning was
used to predict the risk factors of (Type-II) diabetes, and an SVM was used to generate fuzzy
rules from the Pima diabetes dataset [22].
Other researchers used the NB classier to improve the accuracy of predicting heart dis-
ease [23]. Different machine learning techniques, such as Articial Neural Network (ANN),
random forest (RF), and K-means clustering techniques were implemented to predict diabetes.
The ANN technique provided the best accuracy rate (75.7%) in the prediction of diabetes [24].
Some researchers also implemented machine learning techniques to predict hypertension outcomes
based on medical data. In that study, the researchers evaluated four classiers, i.e., SVM, DT, RF,

CMC, 2021, vol.69, no.1 1257
and XGBoost, to meet the desired accuracy level of the prediction system. XGBoost produced
the best results among the four classiers and provided a system accuracy of 94.36%. [25,26].
Other researchers used histopathological data patients who had a lung lobectomy to treat
adenocarcinoma. For both “accidental” models, adjacent to malignancies, the lungs show edema
and fundamental proteinaceous exudates as huge protein globules [27]. The researchers docu-
mented vascular joins with blazing gatherings of brinoid content, multinucleated goliath cells,
and pneumocyte hyperplasia. In addition, some researchers used the ANFIS model to estimate
landslide susceptibility and to develop a model to predict landslides. The ANFIS model was used
to train and validate the dataset [28]. Different ML classiers have been used to develop predictive
models [29,30]. In 2017, researchers proposed an SVM and fuzzy logic-based system automati-
cally block pornographic content on the web. SVMs have also been used in statistical learning
approaches to classify hypothesis test data and compute the error rate using the Gaussian-density
function [31,32].
3 Proposed Methodology
Machine learning classiers, DT, NB, LR, and SMV, are used to classify and categorize the
COVID-19 outbreak in different regions of Pakistan. The proposed system is shown in Fig. 4.
Figure 4: Proposed system for COVID-19 data classication and prediction
3.1 Dataset
The “Corona-Virus Pakistan Dataset 2020” was downloaded from Kaggle [33]. The dataset
contains 13 features that represent the lab tests of suspected, conrmed, and fatal COVID-19
cases per day in the most populated regions of Pakistan (Tab. 1 ). The dataset features are listed in
Tab. 2. The dataset has 315 rows and 13 columns, i.e., 11089 data items. The dataset was checked
for null and missing values of categorical features; none were found. The data distribution of
categorical features, such as Date and Province, are shown in Fig. 5.

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Table 1: Selected regions of Pakistan in dataset
Sr. No. Regions
1. AJK
2. Balochistan
3. GB
4. ICT
5. KP
6. Punjab
7. Sindh
Table 2: Features of COVID-19 dataset
Sr. No. Features
1. Date
2. Province old
3. Suspected cases last date
4. Suspected cases last 24 h
5. Suspected cases cumulative
6. Lab tests last 24 h
7. Lab tests cumulative
8. Conrmed cases last date
9. Conrmed cases last 24 h
10. Conrmed cases cumulative
11. Deaths last date
12. Deaths last 24 h
13. Deaths cumulative
Figure 5: Data distribution of categorical features

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3.2 Dataset Preprocessing
Preprocessing is necessary to avoid misclassied results and errors [34,35]. Data preprocessing
involved data preparation, data exploration, data distribution, and replacing categorical features.
Preprocessing resulted in a clean dataset suitable for classication. This preprocessed dataset is fed
to the machine learning classiers to produce classied results [36].
4 Experimental Analysis and Results
For the classication of the dataset, Google Colab was used for python coding, and dataset
categorization was implemented through MATLAB. The dataset was split into training (70%) and
testing (30%) sets. The metrics used in this work are as follows.
Accuracy =TP +TN
TP +FP +TN +FN (1)
Pecision =TP
TP +FP (2)
Recall =TP
TP +FN (3)
4.1 Decision Tree Classier
The COVID-19 dataset was classied using the DT ID3 classier. The results are shown in
Tab. 3. As can be seen, this classier achieved 100% accuracy, precision, and recall. The confusion
matrix for the DT classier is plotted in Fig. 6a.
Table 3: Results achieved for decision tree classier
Sr. No. Measures Result
1. Accuracy 1.0 => 100%
2. Precision 1.0 => 100%
3. Recall 1.0 => 100%
4.2 Naive Bayes Classier
The NB Classier is implemented on the COVID-19 dataset because it is a continuous dataset.
The NB classier also achieved 100% accuracy (Tab. 4). The confusion matrix for this classier is
shown in Fig. 6b.
4.3 Logistic Regression Classier
The LR classier has been used successfully to predict various diseases [37,38]. The testing
data is predicted for the rst 25 entries. The histogram of the predictions is shown in Fig. 7.
Figs. 8a and 8b depict the confusion matrices for LR and SVM classiers respectively. The
Receiver Operating Characteristics (ROC) plot for the COVID19 dataset, based on true positive
rate and false positive rate, is shown in Fig. 9a. The LR ROC curve covers 91% of the area. The
results obtained for LR are listed in Tab. 5.

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(a) (b)
Figure 6: Confusion matrices for both classiers (a) Decision tree classier (b) Naive Bayesian
classier
Table 4: Results achieved for Naive Bayesian classier
Sr. No. Measures Result
1. Accuracy 1.0 => 100%
2. Precision 1.0 => 100%
3. Recall 1.0 => 100%
Figure 7: Histogram of predicted probabilities
4.4 Support Vector Machine Classier
The linear SVM classier achieved precision of 98%. The ROC curve for multiclass SVM is
depicted in Fig. 9b. It shows that the ROC curve for class-1 covers 100% of the area, while class-2
covers 88% of the area. Tab. 6 lists the SVM results using formulas (1–3).

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(a) (b)
Figure 8: Confusion matrices for (a) LR and (b) SVM classiers
(a) (b)
Figure 9: ROC Curve for (a) LR and (b) SVM classiers
Table 5: Results achieved for logistic regression classier
Sr. No. Measures Result (%)
1. Accuracy 91
2. Precision 86
3. Recall 94
DT and NB classiers yielded 100% accuracy for this dataset. Tab. 7 shows the results for
the DT, NB, LR, and SVM classiers. The classied dataset is input to an ANN (Section 4.5) for
data categorization.

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Table 6: Results achieved for SVM classier
Sr. No. Measures Result (%)
1. Accuracy 97
2. Precision 98
3. Recall 96
Table 7: Comparison of classication results
Sr. No Classier Accuracy (%)
1. Decision tree 100
2. Naive Bayesian 100
3. Logistic regression 91
4. Support vector machine 97
4.5 Articial Neural Network
In the Articial Neural Network training classier, Bayesian regularization is used to cate-
gorize the search space into two classes: normal outbreak and severe outbreak. This classier is
used to categorize the nature of the COVID-19 outbreak in Pakistan based on data collected from
various regions. Fig. 10 shows the COVID-19 dataset simulation architecture.
Figure 10: COVID-19 dataset simulation architecture

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Algorithm 1: Algorithm for Classication
1.Provide the Input Parameters
2.Data Preprocessing
3.Checking of Conditional Probability
4.While (error-rate <threshold-value)
5.Training and Testing of Model
6.If error-rate > threshold-value
7.Back Propagation
8.Weight setting
9.End If
10.End While
11.Neural Network’s Bayesian regularization
12.Classication Results
13.Classication of the Outbreak Nature
The output is labeled 0 or and 1, where 0 represents a normal outbreak and 1 represents
a severe outbreak. The output is labeled based on input parameter values. Tab. 8 shows the
classied, important ranking features of the dataset as inputs selected for the neural network. The
Error Histogram and Regression values are given in Tab. 9.
Table 8: Selected inputs of COVID-19 dataset
Sr. No. Inputs
1. Province old
2. Suspected cases cumulative
3. Lab tests cumulative
4. Conrmed cases cumulative
5. Deaths cumulative
In Tab. 9, from the 852 dataset entries, 596 instances are selected for training, 128 are selected
for validation, and 128 are selected for testing. Furthermore, 50 hidden neurons with one epoch
are used for the neural network. The confusion matrix results demonstrated that the actual
class predicts the predicted class with 99.88% accuracy. This indicates that the BRANN classier
predicts the results accurately for this dataset. Tab. 9 shows that the BRANN classier correctly
categorized 128 data items for the validation and testing process.
Table 9: Bayesian regularization results
Bayesian regularization Samples MSE Regression
Training 596 4.55463e–5 9.99908e–1
Validation 128 0.00000e–0 0.00000e–0
Testing 128 8.41366e–0 2.75570e–1

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Figure 11: Error histogram of Bayesian regularization ANN algorithm
Figure 12: Bayesian regularization regression plot

CMC, 2021, vol.69, no.1 1265
From Fig. 11, it is evident BRANN has 0 errors. This indicates that the neural network ts
the data perfectly. Fig. 12 shows how accurately a neural network determines the function for
regression to analyze the dataset. The actual network details are shown in comparison with the
target output. How accurately a model ts the data is represented through this colored line shown
in the Fig. 12. This line should closely intersect the real output from the left to the right corner of
the regression plot. The above gure shows that the COVID-19 dataset closely ts in the BRANN
model.
Fig. 13 shows the training state of the BRANN (gradient, mu, parameters, the sum of
squared parameters, and validation checks). They all achieve 1000 epochs, which indicates the
good performance of the dataset. Fig. 14 represents the mean square error of the BRANN.
The blue and red training lines represents the testing mean square, and the dotted line represents
the 1000 epochs. The gure listed below shows the best training performance of the BRANN.
Figure 13: Training state of BRANN
Figure 14: Mean square error of neural network BRANN

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Fig. 15 is the confusion matrix of the BRANN classier. The BRANN classier gives 99.88%
accuracy for training, testing, and validation of the classier on the COVID dataset for Pakistan.
The outcome of the dataset is divided into two classes 0 and 1, where 0 denotes that the outbreak
is normal, and 1 represents that the outbreak is severe. Five potential features are selected as input
according to their importance that is classied through ML classiers.
Figure 15: BRANN confusion matrix
The COVID-19 dataset for Pakistan is classied through machine learning techniques, and
their accuracy results are compared. The results show that the NB classier gives 100% accuracy
for this dataset. Therefore, the BRANN best ts the dataset and categorizes the dataset into a
normal class and severe class for the COVID-19 outbreak in Pakistan.
5 Conclusion
The proposed system categorizes the COVID-19 outbreak in Pakistan based on a dataset
collected in different regions of Pakistan. Machine learning classiers play a vital role in the classi-
cation, categorization, and prediction of dangerous diseases such as COVID-19. With the help of
various machine learning techniques, the loss from COVID19 can be minimized in the upcoming
months in Pakistan. First, we classied the COVOD-19 dataset using different machine learning
classiers. Then, the BRANN classier was used to categorize the nature of outbreak as normal
or severe. The experiments show that the BRANN provides a best t regression plot with minimal
error rate. In future, the proposed model can be further tested on a larger dataset [39,40] to test
its scalability.

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Funding Statement: The authors are grateful to the Raytheon Chair for Systems Engineering for
funding.
Conicts of Interest: The authors declare that they have no conicts of interest to report regarding
the present study.
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